Systems and methods for managing populations of utility poles

ABSTRACT

In an example implementation, a method includes receiving, at a processor, historical pole data records representing utility poles and having one or more pole attributes. Likewise, a method includes generating one or more pole subpopulations of historical pole data records having at least one common pole attribute. Further, the method includes performing a predictive algorithm on each pole subpopulation. Finally, the method includes determining, based on a predictive algorithm, the number of poles in the particular subpopulation that are likely to meet a rejection condition within a specified time frame. In another example implementation, a method includes receiving a sample pole data record representing a particular sample data pole and determining the likelihood of the particular sample utility pole meeting a rejection condition within a specified time frame.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit, under 35 U.S.C. §119(e), of U.S.Provisional Patent Application No. 62/182,052 filed Jun. 19, 2015,entitled “SYSTEMS AND METHODS FOR MANAGING POPULATIONS OF UTILITYPOLES,” the entire contents and substance of which is incorporatedherein by reference in its entirety as if fully set forth below.

TECHNICAL FIELD

Aspects of the present disclosure relate to systems and methods formanaging populations of utility pole plants, and more particularly, forpredicting the future condition of utility poles based on theirattributes.

BACKGROUND

Utility companies invest millions of dollars into buildinginfrastructure to deliver services. For example, to deliver electricity,power companies must invest in large distribution networks whichtypically come in the form of transmission lines mounted on utilitypoles. Likewise, telecommunications companies may use utility poles tomount communications cables, such as fiber optic and coaxial cables.Utility poles may also support a wide variety of equipment such astransformers, street lights, traffic lights, cellular network antennas.

Due to the wide array of equipment and services supported by utilitypoles, it is of great economic value that the poles be appropriatelymaintained. However, over time, the poles will naturally degrade andeventually fail. Consequently, poles are regularly inspected to monitorfor potential failures before they occur. But this is a difficult taskas there are more than 150 million wood utility poles and many of themlast in excess of 50 years before degrading to unacceptable levels. Assuch, currently, it is only economically viable for a company to inspecta small percentage of its utility poles every year. Thus, there is aneed for a more cost-effective and far-reaching method of identifyingpotential future utility pole failures or rejections, that can make anassessment of all utility poles in the system, as opposed to just asmall sample size at one given point in time.

SUMMARY

Some or all of the above needs may be addressed by certainimplementations of the disclosed technology. According to an exampleimplementation, a method is provided. The method may include receiving,at a processor, historical pole data records. According to someembodiments, each historical pole data record may represent a particularutility pole and may include data representative of one or more poleattributes for each particular utility pole. Further, the method mayinclude generating, by the processor, one or more pole subpopulations.According to some embodiments, each pole subpopulation may be a subsetof the historical pole data records having at least one common poleattribute. In some embodiments, each pole subpopulation may be a subsetof the historical pole data records having all pole attributes incommon. The method may further include performing, by the processor, apredictive algorithm on each pole subpopulation. Finally, the method mayinclude determining, by the processor, and based on a predictivealgorithm performed on a particular pole subpopulation of the one ormore pole subpopulations, the number of poles in the particularsubpopulation that are likely to meet a rejection condition within aspecific time frame. According to some embodiments, the method mayfurther include generating, by the processor and based on thedetermination of the number of poles in the particular subpopulationthat are likely to meet a rejection condition within a specified timeframe, a recommendation for utility pole replacement or restoration.

According to another example implementation, a method is provided. Themethod may include receiving, at a processor, historical pole datarecords. According to some embodiments, each historical pole data recordmay represent a particular utility pole and may include datarepresentative of one or more pole attributes of a particular utilitypole. Further, the method may include, receiving, at the processor, asample pole data record. According to some embodiments, the same poledata record may represent a particular sample utility pole and mayinclude data representative of one or more pole attributes of theparticular sample utility pole. Further, the method may includegenerating, by the processor, a pole subpopulation. According to someembodiments, the pole subpopulation may be made up of historical poledata records matching the pole attributes of the sample pole datarecord. The method may further include performing, by the processor, apredictive algorithm on the pole subpopulation data. Finally, the methodmay comprise determining, by the processor, and based on the predictivealgorithm, the likelihood of a particular sample utility pole meeting arejection condition within a specified time frame.

According to another example implementation, a system is provided. Thesystem may include a probe for obtaining data from a utility pole, adatabase having historical data, and at least one memory operativelycoupled to at least one processor and configured for storing data andinstructions that, when executed by the at least one processor, causethe system to receive customer asset data, update the database toinclude the received customer asset data, perform a first predictiveanalysis utilizing the customer asset data and historical data, andoutput a first recommendation in response to the first predictiveanalysis.

Other implementations, features, and aspects of the disclosed technologyare described in detail herein and are considered a part of the claimeddisclosed technology. Other implementations, features, and aspects canbe understood with reference to the following detailed description,accompanying drawings, and claims.

BRIEF DESCRIPTION OF THE FIGURES

Reference will now be made to the accompanying figures and flowdiagrams, which are not necessarily drawn to scale, and wherein:

FIG. 1 is a block diagram of an illustrative computer systemarchitecture, according to an example embodiment.

FIG. 2 is a system architecture of a utility pole management system,according to an example embodiment.

FIG. 3A-B are user interfaces displaying results of a method ofpredicting future rejection conditions among a population of utilitypoles, according to an example embodiment

FIG. 4 is a method of predicting future rejection conditions among apopulation of utility poles, according to an example embodiment.

FIG. 5 is a method of predicting future rejection conditions among apopulation of utility poles, according to an example embodiment.

FIG. 6 is a reject curve of rejected poles, according to an exampleembodiment.

FIG. 7 is a reject curve of rejected poles, according to an exampleembodiment.

FIG. 8 is a reject curve of rejected poles, according to an exampleembodiment.

FIG. 9 is a cumulative series of reject curves of rejected poles,according to an example embodiment.

FIG. 10 illustrates a block diagram of utility pole management system,according to an example embodiment.

FIG. 11 illustrates a diagram of a method of using a utility polemanagement system, according to an example embodiment.

FIG. 12 illustrates a diagram of a method of using a utility polemanagement system, according to another example embodiment.

DETAILED DESCRIPTION

In some implementations of the disclosed technology, a utility polemanagement system may receive historical pole data records and makepredictions regarding the number or percentage of utility poles of acertain type that are likely to meet a rejection condition within aspecified time frame. Further, in some implementations of the disclosedtechnology, a utility pole management system may make predictionsregarding what percentage of utility poles predicted to meet a rejectioncondition within the specified time frame may be restorable. Accordingto some embodiments, a utility pole management system may makepredictions regarding the percentage of utility poles that are notpredicted to meet a rejection condition but are nonetheless in a stateof decay. In some embodiments, utility poles in a condition of decay canalso be serviceable or repairable.

Some implementations of the disclosed technology will be described morefully hereinafter with reference to the accompanying drawings. Thisdisclosed technology may, however, be embodied in many different formsand should not be construed as limited to the implementations set forthherein. Although the current disclosure is primarily directed to themanagement of populations of utility poles, it should be understood thatthe systems and methods described herein may be effective in managingpopulations of other wood assets generally requiring continualinspection, maintenance, repair, and replacement and the presentdisclosure is not intended to be limited to management of utility poles.

In the following description, numerous specific details are set forth.It is to be understood, however, that implementations of the disclosedtechnology may be practiced without these specific details. In otherinstances, well-known methods, structures and techniques have not beenshown in detail in order not to obscure an understanding of thisdescription. References to “one implementation,” “an implementation,”“example implementation,” “various implementations,” etc., indicate thatthe implementation(s) of the disclosed technology so described mayinclude a particular feature, structure, or characteristic, but notevery implementation necessarily includes the particular feature,structure, or characteristic. Further, repeated use of the phrase “inone implementation” does not necessarily refer to the sameimplementation, although it may.

Throughout the specification and the claims, the following terms take atleast the meanings explicitly associated herein, unless the contextclearly dictates otherwise. The term “connected” means that onefunction, feature, structure, or characteristic is directly joined to orin communication with another function, feature, structure, orcharacteristic. The term “coupled” means that one function, feature,structure, or characteristic is directly or indirectly joined to or incommunication with another function, feature, structure, orcharacteristic. The term “or” is intended to mean an inclusive “or.”Further, the terms “a,” “an,” and “the” are intended to mean one or moreunless specified otherwise or clear from the context to be directed to asingular form.

As used herein, unless otherwise specified the use of the ordinaladjectives “first,” “second,” “third,” etc., to describe a commonobject, merely indicate that different instances of like objects arebeing referred to, and are not intended to imply that the objects sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

Example implementations of the disclosed technology will now bedescribed with reference to the accompanying figures.

As desired, implementations of the disclosed technology may include acomputing device with more or less of the components illustrated inFIG. 1. It will be understood that the computing device architecture 100is provided for example purposes only and does not limit the scope ofthe various implementations of the present disclosed systems, methods,and computer-readable mediums.

The computing device architecture 100 of FIG. 1 includes a centralprocessing unit (CPU) 102, where computer instructions are processed; adisplay interface 104 that acts as a communication interface andprovides functions for rendering video, graphics, images, and texts onthe display. In certain example implementations of the disclosedtechnology, the display interface 104 may be directly connected to alocal display, such as a touch-screen display associated with a mobilecomputing device. In another example implementation, the displayinterface 104 may be configured for providing data, images, and otherinformation for an external/remote display that is not necessarilyphysically connected to the mobile computing device. For example, adesktop monitor may be utilized for mirroring graphics and otherinformation that is presented on a mobile computing device. In certainexample implementations, the display interface 104 may wirelesslycommunicate, for example, via a Wi-Fi channel or other available networkconnection interface 112 to the external/remote display.

In an example implementation, the network connection interface 112 maybe configured as a communication interface and may provide functions forrendering video, graphics, images, text, other information, or anycombination thereof on the display. In one example, a communicationinterface may include a serial port, a parallel port, a general purposeinput and output (GPIO) port, a game port, a universal serial bus (USB),a micro-USB port, a high definition multimedia (HDMI) port, a videoport, an audio port, a Bluetooth port, a near-field communication (NFC)port, another like communication interface, or any combination thereof.In one example, the display interface 104 may be operatively coupled toa local display, such as a touch-screen display associated with a mobiledevice. In another example, the display interface 104 may be configuredto provide video, graphics, images, text, other information, or anycombination thereof for an external/remote display that is notnecessarily connected to the mobile computing device. In one example, adesktop monitor may be utilized for mirroring or extending graphicalinformation that may be presented on a mobile device. In anotherexample, the display interface 104 may wirelessly communicate, forexample, via the network connection interface 112 such as a Wi-Fitransceiver to the external/remote display.

The computing device architecture 100 may include a keyboard interface106 that provides a communication interface to a keyboard. In oneexample implementation, the computing device architecture 100 mayinclude a presence-sensitive display interface 108 for connecting to apresence-sensitive display 107. According to certain exampleimplementations of the disclosed technology, the presence-sensitivedisplay interface 108 may provide a communication interface to variousdevices such as a pointing device, a touch screen, a depth camera, etc.which may or may not be associated with a display.

The computing device architecture 100 may be configured to use an inputdevice via one or more of input/output interfaces (for example, thekeyboard interface 106, the display interface 104, the presencesensitive display interface 108, network connection interface 112,camera interface 114, sound interface 116, etc.,) to allow a user tocapture information into the computing device architecture 100. Theinput device may include a mouse, a trackball, a directional pad, atrack pad, a touch-verified track pad, a presence-sensitive track pad, apresence-sensitive display, a scroll wheel, a digital camera, a digitalvideo camera, a web camera, a microphone, a sensor, a smartcard,Bluetooth-connected device, and the like. Additionally, the input devicemay be integrated with the computing device architecture 100 or may be aseparate device. For example, the input device may be an accelerometer,a magnetometer, a digital camera, a microphone, and an optical sensor.

Example implementations of the computing device architecture 100 mayinclude an antenna interface 110 that provides a communication interfaceto an antenna; a network connection interface 112 that provides acommunication interface to a network. As mentioned above, the displayinterface 104 may be in communication with the network connectioninterface 112, for example, to provide information for display on aremote display that is not directly connected or attached to the system.In certain implementations, a probe interface 113 is provided that actsas a communication interface and provides functions for obtaining datafrom a probe. In certain implementations, a camera interface 114 isprovided that acts as a communication interface and provides functionsfor capturing digital images from a camera. In certain implementations,a sound interface 116 is provided as a communication interface forconverting sound into electrical signals using a microphone and forconverting electrical signals into sound using a speaker. According toexample implementations, a random access memory (RAM) 118 is provided,where computer instructions and data may be stored in a volatile memorydevice for processing by the CPU 102.

According to an example implementation, the computing devicearchitecture 100 includes a read-only memory (ROM) 120 where invariantlow-level system code or data for basic system functions such as basicinput and output (I/O), startup, or reception of keystrokes from akeyboard are stored in a non-volatile memory device. According to anexample implementation, the computing device architecture 100 includes astorage medium 122 or other suitable type of memory (e.g. such as RAM,ROM, programmable read-only memory (PROM), erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), magnetic disks, optical disks, floppy disks, harddisks, removable cartridges, flash drives), where the files include anoperating system 124, application programs 126 (including, for example,a web browser application, a widget or gadget engine, and or otherapplications, as necessary) and data files 128 are stored. According toan example implementation, the computing device architecture 100includes a power source 130 that provides an appropriate alternatingcurrent (AC) or direct current (DC) to power components.

According to an example implementation, the computing devicearchitecture 100 includes a telephony subsystem 132 that allows thedevice 100 to transmit and receive sound over a telephone network. Theconstituent devices and the CPU 102 communicate with each other over abus 134.

According to an example implementation, the CPU 102 has appropriatestructure to be a computer processor. In one arrangement, the CPU 102may include more than one processing unit. The RAM 118 interfaces withthe computer bus 134 to provide quick RAM storage to the CPU 102 duringthe execution of software programs such as the operating systemapplication programs, and device drivers. More specifically, the CPU 102loads computer-executable process steps from the storage medium 122 orother media into a field of the RAM 118 in order to execute softwareprograms. Data may be stored in the RAM 118, where the data may beaccessed by the computer CPU 102 during execution. In one exampleconfiguration, the device architecture 100 includes at least 128 MB ofRAM, and 256 MB of flash memory.

The storage medium 122 itself may include a number of physical driveunits, such as a redundant array of independent disks (RAID), a floppydisk drive, a flash memory, a USB flash drive, an external hard diskdrive, thumb drive, pen drive, key drive, a High-Density DigitalVersatile Disc (HD-DVD) optical disc drive, an internal hard disk drive,a Blu-Ray optical disc drive, or a Holographic Digital Data Storage(HDDS) optical disc drive, an external mini-dual in-line memory module(DIMM) synchronous dynamic random access memory (SDRAM), or an externalmicro-DIMM SDRAM. Such computer readable storage media allow a computingdevice to access computer-executable process steps, application programsand the like, stored on removable and non-removable memory media, tooff-load data from the device or to upload data onto the device. Acomputer program product, such as one utilizing a communication systemmay be tangibly embodied in storage medium 122, which may comprise amachine-readable storage medium.

According to one example implementation, the term computing device, asused herein, may be a CPU, or conceptualized as a CPU (for example, theCPU 102 of FIG. 1). In this example implementation, the computing device(CPU) may be coupled, connected, and/or in communication with one ormore peripheral devices, such as display. In another exampleimplementation, the term computing device, as used herein, may refer toa mobile computing device such as a smartphone, tablet computer, orwearable computer. In this example implementation, the computing devicemay output content to its local display and/or speaker(s). In anotherexample implementation, the computing device may output content to anexternal display device (e.g., over Wi-Fi) such as a TV or an externalcomputing system.

In example implementations of the disclosed technology, a computingdevice may include any number of hardware and/or software applicationsthat are executed to facilitate any of the operations. In exampleimplementations, one or more I/O interfaces may facilitate communicationbetween the computing device and one or more input/output devices. Forexample, a universal serial bus port, a serial port, a disk drive, aCD-ROM drive, and/or one or more user interface devices, such as adisplay, keyboard, keypad, mouse, control panel, touch screen display,microphone, etc., may facilitate user interaction with the computingdevice. The one or more I/O interfaces may be utilized to receive orcollect data and/or user instructions from a wide variety of inputdevices. Received data may be processed by one or more computerprocessors as desired in various implementations of the disclosedtechnology and/or stored in one or more memory devices.

One or more network interfaces may facilitate connection of thecomputing device inputs and outputs to one or more suitable networksand/or connections; for example, the connections that facilitatecommunication with any number of sensors associated with the system. Theone or more network interfaces may further facilitate connection to oneor more suitable networks; for example, a local area network, a widearea network, the Internet, a cellular network, a radio frequencynetwork, a Bluetooth enabled network, a Wi-Fi enabled network, asatellite-based network any wired network, any wireless network, etc.,for communication with external devices and/or systems.

FIG. 2 is an overview of an exemplary architecture of a utility polemanagement system 200, according to some embodiments. As will bediscussed, a utility pole management system can be used to predictfuture rejection conditions among a population of utility poles,according to some embodiments. In some embodiments, the utility polemanagement system 200 may include a database 202 storing a set ofhistorical data and a database 204 storing a set of customer asset data.Historical data may include data records indicative of the features,attributes, and historical conditions of a set of assets (e.g., utilitypoles) that may belong to a plurality of different owners or customers.For example, historical data may include data indicative of theattributes or features of a set of utility poles and the degradation,repairs, and replacements of those utility poles over a period of time(e.g., the last 50 years). According to some embodiments, customer assetdata may include data on a particular group of utility poles, forexample, the set of currently deployed utility poles (“in-service”)owned by a particular customer. Furthermore, customer asset data mayinclude a set of data records pertaining to the customer assets, whereeach record provides information and attributes about a particularasset, such as the asset's location (e.g., GPS coordinates, zip code orthe like), installation date or year, material, and other such featuresthat will be described in greater detail below. In some embodiments,customer asset data 204 may be updated periodically or in real-time asthe currently deployed assets of a customer change or experiencerepairs.

According to some embodiments, a database 206 can receive and/or storehistorical data and customer asset data from databases 202, 204. Thoughshown separately in FIG. 2, it will be understood that in someembodiments, databases 202, 204 may be simply replaced by a singledatabase 206 in some embodiments, and the database 206 may receivehistorical data and customer asset data from another source, such as aremote computer device or a user input. Historical data may includehistorical pole data collected on a wide variety of utility pole typesand may contain information indicative of the attributes or features ofeach utility pole, such as, for example, but not limited to, the age ofthe pole, the species of wood it is made from, the location of the pole,the decay zone of the pole, the program inspection type of the pole, theoriginal treatment type of the pole, the age of the pole when it reacheda rejection or failure condition, and much more. Customer asset data caninclude similar data regarding attributes and features of a customer'sdeployed or estimated utility pole assets. In some embodiments, adatabase of the system (e.g. database 202, 204, 206) may be implementedon a computing device and may utilize SQL or another suitable languagefor database management.

In some embodiments, the utility pole management system 200 may includea predictive algorithm module 208 that may include a library or databaseof pole condition prediction algorithms. According to some embodiments,the predictive algorithm module 208 may perform one or more predictivealgorithms on the historical data and/or the customer asset data togenerate predictions on future pole conditions of the customer assets.In some embodiments, the predictive algorithm module 208 may output theresults of the predictive algorithms to an application 210. In someembodiments, the application 210 may include a user interface ordashboard that may allow a user to view and interact with the dataand/or results of the pole condition predictive algorithm. According tosome embodiments, the application 210 may output or display thepredicted outcomes 212, on for example, a user interface. For example,the application 210 may generate reports and/or display the results. Thesystem 200 may generate various categories of predicted outcomes 212,including for example, financial outcomes (e.g., budgets and predictedcosts relating to predicted repairs or replacements), risk (e.g,volatility in costs), program valuation (e.g, PVRR, ROE, etc.), or anoperational plan.

In some embodiments, the application 210 may receive (e.g., via userinput) various parameters to model “what if” scenarios for differentprogram and financial variables. For example, a user may inputparameters such as, but not limited to, pole depreciation, cycle length,replacement cost, restoration cost, reject management (e.g., “80%Restore/20% Replace”), average inspection cost, average treatment cost,the percentage of poles treated, the percentage expense in replacement,the allowed rate of return, the number of poles replaced in the pastyear, and the inspection type. FIGS. 3A-B illustrate example embodimentsof a dashboard or user interface 300, 302 that can display results andpredictions generated by the application 210 and that are useful inplanning for future utility pole maintenance and replacement programs.For example, the user interface 300, 302 can display the financial,risk, and asset condition outcomes for each “what if” scenario performedby the system 200.

To generate such financial predictions, it may first be necessary forthe system 200 to generate predictions regarding the future condition ofthe set of utility poles at issue. In particular, it may be necessaryfor the system 200 to generate predictions regarding when one or morepoles of the set of utility poles will be considered to be in arejection condition. According to some embodiments, a rejectioncondition may be a condition of a utility pole that does not meet thestrength requirements of the National Electrical Safety Code. Morespecifically, the NESC states that a pole should be rejected if theremaining strength is less than two-thirds of the original requiredstrength. Thus, according to some embodiments of the present disclosure,the system 200 can determine that a utility pole is in a rejectioncondition if the utility pole's strength is predicted to be less thantwo-thirds of the originally required strength, as determined forexample, by a predictive algorithm of the system 200. Embodiments of thepresent disclosure can be used to project the future condition of a polebased on a variety of maintenance program types. For example, in someembodiments, the system 200 can predict how many poles have no decay,how many poles are decayed but serviceable, and how many poles are belowcode strength requirements (i.e., are in a rejection condition).

FIG. 4 is a flow diagram of a method 400 for predicting the number orpercentage of utility poles that are likely to meet a rejectioncondition within a specified time frame, according to an exampleimplementation. As shown in FIG. 4, and according to an exampleimplementation, the method 400 can include receiving 402, by aprocessor, historical pole data records. According to some embodiments,each historical pole data record can represent a particular utility poleand each historical data pole record can include one or more poleattributes of the particular utility pole. Pole attributes can include,but are not limited to, for example, pole age (or year manufactured),decay zone, species of wood, program inspection type, original treatmenttype, previous treatment type, rejection age, job number, region,district, area, state, county, city, grid, line, substation, section,township, range, map number, feeder name, feeder number, circuit name,circuit number, supervisor, foreman, crew ID, Applicator ID, Week EndingDate, Current Day of Week, Date, GPS Timestamp, State Tracking ID,Location ID, GPS ID, Structure ID, Added Structure ID, Inspection ID,Condition ID, GPS coordinate, distance, bearing, pole number, Alt Polenumber, Customer Data ID, Multiple Pole Desc, Owner, Manufacturer, YearManufactured (actual or estimated), Length/Class (actual or estimated),Length, Class, Species/Material, Previous Cycle Information, LastInspected By, Year Last Inspected, Previous Restored Year, PreviousRestoration Method, Pole Type, RUS Codes, Pole Accessibility, OriginalG/L Circumference, SR Circumference—this cycle, Shell Rot No Reduction,Strength Required, StrengthCalc, LoadCalc, Inspection and TreatmentActivities, Inspection Comments, Can Not Treat Reason, Primary RejectionReason, Priority Pole, Restorable—Decay Condition, Restorable—CustomerSpec, Recommended Restoration Method, Restoration Height, Location,Cross Street, RUS Code Billable, LoadCalc Billable, Quantity, MeasuredValue, Maintenance ID, DuraFume, Flurods, MITC Fume, Anchor Eye Inspect,Anchor Eye Inspect—Corroded, Anchor Eye Inspect—Good, Anchor EyeInspect—Rejected, Apply Fireguard, Clear Buried Anchor, Danger SignsInstall, Danger Signs Remove, Ground Resistance Measurement, Ground RodInstall, Groundwire Molding Install, Groundwire Repair, Guy MarkerInstall—Customer, Guy Marker Install—Company, Guy Wire Tail Trim, LineClearance Measurement, Pole Number Install, Pole Reflector Install, PoleStencil Install, Riser Guard Install, Step Removal, Visibility StripsInstall, Hourly Rate, Hourly Rate—Crew Member, Hourly Rate—Crew MemberOT, Hourly Rate—Foreman, Hourly Rate—Foreman OT, Hourly Rate—Foreman andTruck, and Hourly Rate—Truck.

The method 400 can include generating 404 one or more polesubpopulations, wherein each pole subpopulation is made up of a subsetof the historical pole data records having at least one common poleattribute. For example, a pole subpopulation could be made up of all ofthe pole records where the species of wood is southern yellow pine andthe pole age is 10 years. In some embodiments, historical pole datarecords can be part of many distinct but overlapping pole subpopulations(e.g., a pole having attributes X, Y, and Z may be included in a firstpole subpopulation including attributes X and Y, and a second polesubpopulation including attributes Y and Z). According to someembodiments, each pole subpopulation may be made up of a subset of thehistorical pole data records having all common attributes, such thateach historical pole data record is only part of one pole subpopulation(e.g., a pole having attributes X, Y, and Z is only in a firstsubpopulation including attributes X, Y, and Z).

The method can include performing 406, by the processor, a predictivealgorithm on each pole subpopulation. Thus, the method may includeseparately analyzing data on each set of utility poles that have similarcharacteristics (e.g., age, wood species type, decay zone).

In some embodiments, the predictive algorithm can assign a weight factoror coefficient to each pole attribute that may represent thesignificance of each pole attribute's effect in contributing to thatpole subpopulation's degradation towards reaching a rejection condition.The weighting of these factors may vary from subpopulation tosubpopulation. For example, yellow pine may rot significantly faster indecay zone 1 than it does in decay zone 2, and thus the coefficientswould be different in each of those scenarios. It will be understood bythose of skill in the art that these weighting factors may change overtime as the number of historical pole data records changes, and that ingeneral, having a greater number of historical pole data records willlead to more accurate future predictions.

The method can include determining 408, by the processor and based onthe predictive algorithm performed on data of a particular polesubpopulation of the one or more subpopulations, the number of poles inthe particular subpopulation that are likely to meet a rejectioncondition in a specified time frame.

According to some embodiments, the disclosed system may use acombination of methodologies to create, test, optimize, and executepredictive algorithms. In some embodiments, these methodologies caninclude, for example: an ANOVA study on individual variables, acovariance study on multiple variables, construction of multinomialregression structures using correlation and variance study results,solving of final algorithm variable coefficients using a geneticalgorithm or simulated annealing, construction and training of neuralnetworks, construction and training of random decision forests, trainingof support vector machine algorithms, logistical regressions, the use ofgradient boosting in regressions, and the application of rejectionsampling to customized historical datasets using an inverse distanceweighting algorithm. It should be understood that various embodiments,the predictive algorithms described herein may use some or all of thesetechniques, as well as other statistical, mathematical, modeling, orother such techniques known in the art.

FIG. 5 is a flowchart of a method 500 for predicting the likelihood of aparticular individual utility pole meeting a rejection condition withina specified time frame, according to an example implementation. As shownin FIG. 5, and according to an example implementation, the method 500can include receiving 502, by a processor, historical pole data records.The method 500 can include receiving 504, by the processor, a samplepole data record representing a particular sample utility pole. Forexample, the sample utility pole may be a particular utility pole inwhich it is desirable to determine the probable remaining useful life.The method 500 can include generating 506, by the processor, a polesubpopulation. According to some embodiments, the pole subpopulation mayinclude poles corresponding to historical data records including poleattributes that match the pole attributes of the sample pole datarecord. For example, if the sample utility pole is a yellow pine that is20 years old in decay zone 3, then the subpopulation may be made up ofhistorical pole data records of yellow pine utility poles that are 20years old in decay zone 3. The method 500 can include performing 508, bythe processor, a predictive algorithm on the pole subpopulation. Forexample, the processor can generate a rejection curve that displays thepercentage of poles of the subpopulation that are in, or are predictedto be in, a rejection condition across a specified time period. Themethod 500 can include determining 510, by the processor, the likelihoodof the particular sample utility pole meeting a rejection conditionwithin a specified time frame. According to some embodiments, theprocessor can make this determination based on the predictive algorithmof the subpopulation. For example, the processor may determine thatthere is a 50% probability that the sample utility pole will meet arejection condition after 20 years, and an 80% probability after 40years. The method 500 may be repeated several times using sample poledata records representing different individual utility poles of a groupof utility poles to assess the future condition of the group of utilitypoles.

In some embodiments, the disclosed systems and methods may also includegenerating a predicted degradation curve for a particular type ofutility pole, or a utility pole having a particular set of poleattributes, by performing a predictive algorithm on the data records ofutility poles having similar features. A degradation curve can show thepredicted state of the utility pole over time, and predict, for example,at what point the utility pole will meet a rejection condition, at whatpoint the utility pole will be in a condition where restoration may benecessary, or at what point the utility pole's condition may be beyondrepair. In some embodiments, an algorithm may be used on utility poledata records with the same set of pole attributes, but with theadditional attribute of having been restored or repaired at some pointin time. According to some embodiments, the system may utilize thealgorithm of utility pole data records having a repair/restorationattribute to perform a prediction on the increase in lifespan of autility pole having similar features, if such a repair were to beperformed on that utility pole. In some embodiments, a modifieddegradation curve for the utility pole may be generated based on thepredicted increase in lifespan resulting from a similar repair orrestoration being performed on the utility pole.

In accordance with some embodiments, the methods described herein maygenerate a reject curve similar to the examples shown in FIGS. 6-9,which show the cumulative poles rejected (%) over a span of 100 years.FIG. 6 shows an example rejection curve of cumulative rejections by agefor a population of 266,000 utility poles having the features (withrespective coefficients in parentheses): Decay Zone 5 (−0.46), ProgramType F (−0.93), Pentacholorophenol (−1.35), Southern Pine (−1.35), andNot Previously Treated (−1.35). From the graph shown in FIG. 6, it canbe seen that roughly 70% of this subpopulation (i.e., decay zone 5, F,Pentacholorophenol, Southern Pine, not previously treated) will be in arejection condition after approximately 40 years. FIGS. 7 and 8 showsimilar examples of rejection curves for different subpopulations ofutility poles. FIG. 7 shows an example rejection curve of cumulativerejections by age for a population of 224,000 utility poles having thefeatures: Decay Zone 2 (−0.32), Program Type F (−1.44), Pentacholophenol(−1.44), Southern Pine (−1.44) and Not Previously Treated (−1.44). FIG.8 shows an example rejection curve of cumulative rejections by age for apopulation of 33,000 utility poles having the features: Decay Zone 2(−2.04), Program Type P2 (−0.84), Pentacholorphenol (−2.67), Western RedDedar (−3.65), and Not Previously Treated (−1.35). According to someembodiments, some or all rejection curves for different subpopulationsof pole data records may be aggregated to form a total picture of all ofthe utility poles in the system. For example, FIG. 9 shows an example ofa cumulative distribution function, or, the cumulative rejection curvesacross various subpopulations of utility poles. According to someembodiments, the variable coefficients may continuously change asadditional inspection data is introduced into the model, furtheroptimizing the algorithm. Rejection curves such as these may be used inplanning utility pole inspection, repair, and replacement programs.

It will be understood by those of skill in the art, that the methodsdisclosed herein may be utilized to make predictions regarding thefuture condition of one or more utility poles in a manner that willenable a company to implement improved pole maintenance and replacementprograms. Furthermore, it will be understood that these methods andsystems disclosed herein may be modified or adapted to not only generatepredictions with regards to rejection conditions, but with regard tomany other conditions as well. For example, the methods and systems maybe used to estimate or predict what percentage of actual or predictedutility poles meeting a rejection condition in a specified time frameare predicted to be in a condition such that they are restorable.According to some embodiments, a utility pole may be restorable if it ispredicted to meet the structural engineering criteria that permit theinstallation of a steel truss restoration system, per applicableconstruction and engineering codes. In some embodiments, a utility polemay be a restoration candidate if it has adequate sound wood aboveground to transfer the load from the pole to the steel truss.Furthermore, the methods and systems may be utilized to determine whatpercentage of the actual or predicted utility poles that do not meet therejection condition in a specified time frame, will nonetheless bepredicted to be in a condition of decay. In some embodiments, themethods disclosed herein can identify which particular utility poles ofa given subpopulation are most likely to meet a rejection condition andthus, inspectors can focus their attention on those particular poles.For example, in some embodiments, the system can apply a discreteprobability of a specific utility pole meeting a rejection condition atany future specified year, using the methodologies and criteriaspecified herein.

According to some embodiments, a predictive algorithm as disclosedherein can be an algorithm designed to make a single prediction. In someembodiments, the results of one or more algorithms may be combined. Insome embodiments, a predictive algorithm may utilize an assumption thatany utility pole that will be replaced in the future will be replacedwith a new utility pole having the exact same pole attributes, otherthan pole age. In some embodiments, a predictive algorithm may utilizean assumption that any utility pole that will be replaced in the futurewill be replaced with a new utility pole having a specific set ofascribed attributes. According to some embodiments, the ascribedattributes can be based on a pole purchasing policy that is eithercurrently in use or is under evaluation. In some embodiments, thesystems and methods presented herein can generate reports that provideinformation such as, but not limited to, the number, type, and/orpercentage of utility poles that are likely to need to be replaced orrestored within a specified number of years, the predicted cost ofreplacing and/or repairing the necessary utility poles in a given year,indications of the pole attributes that lead to the longest useful life,indications of the pole attributes that provide the best value perdollar spent, and various other reports, statistics and data that wouldbe useful in managing the maintenance of a large number of utility poleplants.

Furthermore, embodiments of the present disclosure can provide models ofthe financial impacts relating to the predicted degradation of a utilitypole or a set of utility poles. Such models may assist the owner of theutility poles in planning replacement programs and in making financialdecisions. According to some embodiments, the financial impacts modeledby the system can include the Rate Base, CapEx budgets, and OpExbudgets. As previously described with respect to FIG. 3, an application210 of the present disclosure can enable a user to run various “what if”scenarios for different program and financial variables, such as forexample, inspection type, cycle times, costs, and cost of capital. Insome embodiments, the system may be configured to output variousfinancial predictions, including, but not limited to: 1)program/investment NPV, FV, PI, ROE, PVRR, 2) risk metrics such as i)expected volatility in future required CAPEX and other expenditures andii) unmitigated operational risks (e.g., unobserved reject rate,exposure rates), 3) (future) annual inspection, restoration andreplacement budgets, 4) asset lifecycle costs and (cost) efficiencymetrics, and 5) regulatory metrics/KPIs such as present value of revenuerequirements (PRVV). Such financial predictions, tools, and reports canassist utility pole owners in optimizing the financial aspect of utilitypole management programs by facilitating optimal return on equity,continuous recapitalization and efficient through rates, efficientcapital allocation, risk management (e.g., budget volatility, costcontrol/hedge, overall program stability), and understanding of blackswans (i.e. correlations amongst pole failures).

According to some embodiments, a utility pole management system 200 mayinclude a real-time decision framework that can utilize the systems andmethods described herein. For example, according to some embodiments, areal-time decision framework can be integrated into a system thatutilizes the prediction capabilities described herein to make real-timepredictions about utility poles that either are or will be in arejection condition or in need of repair. Therefore, in someembodiments, a system of the present disclosure can be capable ofoutputting notifications and other data relating to real-time poleconditions to a mobile device of the service technicians. In someembodiments, responsive to receiving the notifications and other data,the mobile device of the service technician can display instructions orother information regarding which utility poles should be inspected,repaired or replaced. Accordingly, a real time decision framework canenable the real time prioritization of inspections, repairs andreplacements of utility poles.

As shown in FIG. 10, according to some embodiments, a real-time decisionframework may be enabled by a utility pole management system 200 thatinclude a remote server 1000 that is in communication with a mobiledevice 1010 via a network 1020 (e.g., the internet). A mobile device1010 can be, for example, a laptop, tablet computer, or mobile phone ofa technician that may be connected to one or more probes or sensors.Generally speaking, the mobile device 1010 may collect data inreal-time, either through one or more probes and/or sensors that may beintegrated with the mobile device 1010, or through data entry of atechnician observing the condition of a particular utility pole eithervisually or using a detached probe or sensor. Newly gathered data may beadded to the database 206 of the utility pole management system 200, andthe predictive algorithm module 208 may output new predictions orrecommended actions based on the updated data.

According to some embodiments, either or both of the remote server 1000and the mobile device 1010 may include some or all of the elements ofthe computing device architecture 100 of FIG. 1. According to someembodiments, the remote server 1000 may include some or all aspects ofthe utility pole management system 200 shown in FIG. 2. In someembodiments, the remote server 1000 may include a database 206, whichmay comprise data records including historical data 202 and customerasset data 204. The remote server 1000 may update customer asset data204 in the database 206 in response to receiving new customer asset datafrom, for example, one or more mobile devices 1010 (e.g., in response totechnicians taking measurements from utility poles in the field).Furthermore, in some embodiments, the remote server 1000 may include apredictive algorithm module 208 for performing predictive algorithms asdescribed above. According to some embodiments, in response to receivingmeasurements or an updated asset data record from a mobile device 1010,the remote server 1000 may execute instructions to cause the predictivealgorithm module 208 to perform a predictive analysis using the updateddata. Thus, in this way, the mobile computing device can output areal-time solution or prediction of utility pole conditions. Accordingto some embodiments, the real-time solution or prediction can be used toinstruct a technician one or more decisions, such as for example, butnot limited to, what maintenance(s) to perform, what other maintenanceactions should be taken, the future date at which other maintenance(s),repair(s), or restoration(s) should be performed, and the future date atwhich the asset should be replaced. Such instructions or recommendationsmay be sent from the remote server 1000 to the mobile device 1010 fordisplay to a user. In some embodiments, the remote server 1000 mayinclude an application 210 for generating and displaying reports andother date and for allowing a user to interface with the utility polemanagement system 200 and run “what if” scenarios as described above.

According to some embodiments, the mobile device 1010 may include aprobe 1012 for sensing and collecting data measurements from utilitypoles or other assets. In some embodiments, a probe 1010 may be a devicethat may be designed to be inserted into a utility pole to obtainmeasurements or other data. For example, in some embodiments, the probe1012 may be the tool described in U.S. patent application Ser. No.14/625,303. In some embodiments, a probe 1010 may be capable ofmeasuring the hardness, thickness, moisture content, temperature, orother such aspects of a utility pole. In some embodiments, a probe 1010may be capable of capturing an external or internal image of a portionof a utility pole. In some embodiments, a probe 1010 may be capable ofdetecting weaknesses, rot, holes, or other damage to a utility pole1010. According to some embodiments, a customer asset data record of thecustomer asset data 204 may be updated in response to the measurementsobtained by the probe 1012 of the mobile device 1010. In someembodiments, the mobile device 1010 may transmit the measurements to theremote server 1000 and the remote server 1000 may update the customerasset date record in the database 206. In some embodiments, the mobiledevice 1010 may include a local copy of the database 206 and it mayupdate a customer asset data record on the mobile device 1010. In someembodiments, the mobile device 1010 may receive updates to the localcopy of the database 206 from the remote server 1000. For example, afirst mobile device 1010 may receive newly acquired utility pole data,measurements, or one or more updated asset data records from the remoteserver 1000, in response to the remote server 1000 receiving the newdata from a second mobile device 1010. Furthermore, in some embodiments,a mobile device 1010 may include either or both of a predictivealgorithm module 208 and an application 210. Accordingly, in someembodiments, a mobile device 1010 may be enabled to locally updatedatabase 206 records, perform a new predictive analysis on the updateddata, and output results or recommendations through the application 210,without having to communicate with the remote server 1000. In someembodiments, the mobile device 1010 may periodically communicate withthe remote server 1000 to send the remote server the probe measurementsor updated asset data record. In some embodiments, the mobile device1010 may be in constant communication with the remote server 1000,provided that a network connection 1020 is available.

FIG. 11 illustrates a diagram of a method of using a utility polemanagement system, according to an example embodiment wherein the remoteserver 1000 includes the database 206 and predictive algorithm module208, but the mobile device 1000 does not include the database 206 andpredictive algorithm module 208. The method can include obtaining 1102,by the mobile device 1010, new customer asset data from a probe 1012 orother sensor. The mobile device 1010 may then transmit 1104 the newcustomer asset data to the remote server 1000 via the network 1020.According to some embodiments, the remote server 1000 may update 1106the database, perform a predictive analysis utilizing the new data, andgenerate a prediction or recommendation. In some embodiments, the remoteserver 1000 may transmit 1108 the prediction or recommendation to themobile device 1010 for display to a technician. In this way, atechnician using the mobile device 1010 may receive updatedrecommendations from the utility pole management system 200 in real-timein response to obtaining updated asset data measurements.

FIG. 12 illustrates a diagram of a method of using a utility polemanagement system, according to an example embodiment wherein the mobiledevice 1010 includes a copy of the database 206 and predictive algorithmmodule 208. According to some embodiments, the remote server 1000transmits 1202 a copy or an updated copy of the database 206 and/orpredictive algorithm module 208 to the mobile device via the network1020. In some embodiments, the mobile device 1010 may obtain 1204 newcustomer asset data by, for example, taking measurements from a utilitypole with a probe 1012. Furthermore, the mobile device 1010 may update alocal copy of the database 206 with the new customer asset data.According to some embodiments, the mobile device 1010 may optionallytransmit 1206 the new customer asset data to the remote server 1000,either in response to obtaining the new customer asset data or periodiccommunications with the remote server 1000. In some embodiments, themobile device 1010 may locally perform 1208 a predictive analysisutilizing the new data and generate a prediction or recommendation to bedisplayed by the mobile device 1010 for a technician. Thus, according tovarious embodiments of a utility pole management system 200, real-time,updated predictions and/or recommendations may be generated either atthe remote server 1000 or the mobile device 1010 in response toobtaining new customer asset data by a probe 1012 or data entered intothe mobile device 1010 by a technician.

Certain implementations of the disclosed technology are described abovewith reference to block and flow diagrams of systems and methods and/orcomputer program products according to example implementations of thedisclosed technology. It will be understood that one or more blocks ofthe block diagrams and flow diagrams, and combinations of blocks in theblock diagrams and flow diagrams, respectively, can be implemented bycomputer-executable program instructions. Likewise, some blocks of theblock diagrams and flow diagrams may not necessarily need to beperformed in the order presented, or may not necessarily need to beperformed at all, according to some implementations of the disclosedtechnology.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the sequence diagramblock or blocks.

Implementations of the disclosed technology may provide for a computerprogram product, comprising a computer-usable medium having acomputer-readable program code or program instructions embodied therein,said computer-readable program code adapted to be executed to implementone or more functions specified in the sequence diagram block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational elements or steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide elements or steps for implementing the functionsspecified in the flow diagram block or blocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specified functionsand program instruction means for performing the specified functions. Itwill also be understood that each block of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, can be implemented by special-purpose, hardware-based computersystems that perform the specified functions, elements or steps, orcombinations of special-purpose hardware and computer instructions.

While certain implementations of the disclosed technology have beendescribed in connection with what is presently considered to be the mostpractical and various implementations, it is to be understood that thedisclosed technology is not to be limited to the disclosedimplementations, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the scope ofthe appended claims. Although specific terms are employed herein, theyare used in a generic and descriptive sense only and not for purposes oflimitation.

This written description uses examples to disclose certainimplementations of the disclosed technology, including the best mode,and also to enable any person of ordinary skill to practice certainimplementations of the disclosed technology, including making and usingany devices or systems and performing any incorporated methods. Thepatentable scope of certain implementations of the disclosed technologyis defined in the claims, and may include other examples that occur tothose of ordinary skill. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

What is claimed is:
 1. A method comprising: receiving, at a processor,historical pole data records, each historical pole data recordrepresenting a particular utility pole and comprising datarepresentative of one or more pole attributes of the particular utilitypole; generating, by the processor, one or more pole subpopulations,each pole subpopulation comprising a subset of the historical pole datarecords comprising at least one common pole attribute; performing, bythe processor, a predictive algorithm on each pole subpopulation; anddetermining, by the processor, and based on a predictive algorithm on aparticular pole subpopulation of the one or more pole subpopulations,the number of poles in the particular subpopulation that are likely tomeet a rejection condition within a specified time frame.
 2. The methodof claim 1, wherein the one or more pole attributes include one or moreof at least: pole age; decay zone; program inspection type; woodspecies; original treatment; and previous supplemental treatment type.3. The method of claim 1, wherein each pole subpopulation furthercomprises a subset of historical pole data records having the same poleattributes.
 4. The method of claim 1, further comprising, aggregating,by the processor, the total number of poles that will be rejected in apredetermined time frame across all subpopulations.
 5. The method ofclaim 1, further comprising, generating, by the processor and based onthe determination of the number of poles in the particular subpopulationthat are likely to meet a rejection condition within a specified timeframe, a recommendation for utility pole replacement or restoration. 6.The method of claim 1, further comprising, determining, by theprocessor, and based on a predictive algorithm on a particular polesubpopulation of the one or more pole subpopulations, the number ofpoles in the particular subpopulation that are likely to be in a stateof decay within a specified time frame.
 7. The method of claim 1,further comprising, determining, by the processor, and based on apredictive algorithm on a particular pole subpopulation of the one ormore pole subpopulations, the number of poles in the particularsubpopulation that are likely to have no decay within a specified timeframe.
 8. A method comprising: receiving, at a processor, historicalpole data records, each historical pole data record associated with aparticular utility pole and comprising data representative of one ormore pole attributes of the particular utility pole; receiving, at theprocessor, a sample pole data record, the sample pole data recordrepresenting a particular sample in-service utility pole and comprisingdata representative of one or more pole attributes of the particularin-service sample utility pole; generating, by the processor, a polesubpopulation, the pole subpopulation comprising historical pole datarecords matching the pole attributes of the sample pole data record;performing, by the processor, a predictive algorithm on the polesubpopulation; and determining, by the processor, and based on thepredictive algorithm, the likelihood of the particular sample utilitypole meeting a rejection condition within a specified time frame.
 9. Themethod of claim 8, wherein performing a predictive algorithm includesgenerating a rejection curve.
 10. The method of claim 9, wherein therejection curve represents the percentage of utility poles of thesubpopulation that are predicted to be in a rejection condition across aspecified time period.
 11. A system comprising: a probe for obtainingdata from a utility pole; a database having historical data; at leastone memory operatively coupled to at least one processor and configuredfor storing data and instructions that, when executed by the at leastone processor, cause the system to: receive, from a remote server,customer asset data; update the database to include the receivedcustomer asset data; perform a first predictive analysis utilizing thecustomer asset data and historical data; and output for display, a firstrecommendation in response to the first predictive analysis.
 12. Thesystem of claim 11, wherein the instructions further cause the systemto: receive, from the probe, measured customer asset data; update thedatabase to include the measured customer asset data; perform a secondpredictive analysis utilizing the customer asset data and historicaldata; and output for display, a second recommendation in response to thesecond predictive analysis.
 13. The system of claim 12, wherein themeasured customer asset data is data that is measured from a utilitypole by the probe.
 14. The system of claim 13, wherein the measuredcustomer asset data represents the strength of a portion of the utilitypole.
 15. The system of claim 13, wherein the customer asset datacomprises a plurality of customer asset data records pertaining to acustomer's deployed utility poles, wherein each data record isassociated with a particular deployed utility pole and includes one ormore pole attributes of the utility pole.
 16. The system of claim 15,wherein the historical data comprises a plurality of historical datarecords pertaining to a historical utility poles, wherein each datarecord is associated with a particular historical utility pole or groupof historical utility poles and includes one or more pole attributes ofthe utility pole or group of historical utility poles, respectively. 17.The system of claim 16, wherein the first and second predictive analysesinvolve performing a predictive algorithm on a group of customer assetdata records and/or historical data records, wherein each data record ofthe group has one or more pole attributes in common.
 18. The system ofclaim 17, wherein the one or more pole attributes in common includes oneor more of: pole age; decay zone; program inspection type; wood species;original treatment; and previous supplemental treatment type.